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A major challenge to contemporary education is to meet the Government’s directive, depicted in OFSTED guidelines and the Department for Education’s Teacher Standards that all our learners make progress, are autonomous and are able to engage in independent learning. However they offer no guidance as to how this can be achieved. The research has built on earlier theories to close the gap between Government measurements of the quality of teaching and twenty-first century educational theories, with particular focus on learning talk. The primary intention of this research was to determine the impact that dynamically dialogic learning conversations, that is learning talk, have on deepening learning, and how they may be used to enable teachers to meet OFSTED’s requirement for all students to make progress. The data for this case study was collected through a process of lesson observations, interviews and focus-group discussions over a period of one year. Sixteen lessons were video-recorded for a variety of topics and the recordings were analysed in depth against established theories of learning and the complex patterns and relationships between the different types of student and teacher learning talk observed in the classroom. The outcome of the analysis is a set of observable characteristics of learning talk which form an Observation Database. The findings support the premise that learning talk in the classroom leads to deeper learning. The Observation Database contains of a set of tools for observing, evaluating and enabling learning talk in the classroom and therefore offers teachers the opportunity to demonstrate OFSTED criteria. The process of developing the Observation Database and the tools developed have been shared both locally and nationally to heighten awareness of learning talk in the classroom and its link to deeper learning.

Through an exploratory qualitative, interpretive frame that employed an ethnographic methodological approach, this research focuses on teaching assistants (TAs) teaching in a lab, tutorial, or discussion group. Nine TAs share their learning journey as they begin teaching in higher education. The theoretical lens that frames this research is workplace learning. Interviews, observations, video-recordings, field notes, and learning diaries were subjected to thematic analysis, looking for dominant themes associated to TAs’ characteristics, their learning process related to teaching, and the knowledge they developed about teaching and student learning. Key findings include the recognition that TAs bring robust conceptions and dispositions to their first teaching position that is approached from a student subject position as they are becoming teachers. As TAs are being teachers, they control their self-directed learning process as they make decisions on-the-fly within a diverse learning environment that ranges from expansive to strategic to restrictive affordances. Coupled with a discretionary reflective practice, TAs’ knowledge development about teaching and student learning is solely dependent upon their experience, making forthcoming development of knowledge about teaching and student learning relegated to chance. This focus on TAs’ learning in the workplace illuminates the need for a deep learning approach to learning about teaching and student learning that needs to begin with graduate students’ first appointment as a TA. In addition, this deep learning approach needs to be encased in an expansive learning environment that provides opportunities for continuous support through various forms of mentorship, instruction, and development of reflective practice. / Graduate

This dissertation addresses the task of learning to rank, both in the supervised and unsupervised settings, by exploiting the interplay of convex functions, monotonic mappings and their fixed points. In the supervised setting of learning to rank, one wishes to learn from examples of correctly ordered items whereas in the unsupervised setting, one tries to maximize some quantitatively defined characteristic of a "good" ranking. A ranking method selects one permutation from among the combinatorially many permutations defined on the items to rank. Accomplishing this optimally in the supervised setting, with minimal loss in generality, if any, is challenging. In this dissertation this problem is addressed by optimizing, globally and efficiently, a statistically consistent loss functional over the class of compositions of a linear function by an arbitrary, strictly monotonic, separable mapping with large margins. This capability also enables learning the parameters of a generalized linear model with an unknown link function. The method can handle infinite dimensional feature spaces if the corresponding kernel function is known. In the unsupervised setting, a popular ranking approach is is link analysis over a graph of recommendations, as exemplified by pagerank. This dissertation shows that pagerank may be viewed as an instance of an unsupervised consensus optimization problem. The dissertation then solves a more general problem of unsupervised consensus over noisy, directed recommendation graphs that have uncertainty over the set of "out" edges that emanate from a vertex. The proposed consensus rank is essentially the pagerank over the expected edge-set, where the expectation is computed over the distribution that achieves the most agreeable consensus. This consensus is measured geometrically by a suitable Bregman divergence between the consensus rank and the ranks induced by item specific distributions Real world deployed ranking methods need to be resistant to spam, a particularly sophisticated type of which is link-spam. A popular class of countermeasures "de-spam" the corrupted webgraph by removing abusive pages identified by supervised learning. Since exhaustive detection and neutralization is infeasible, there is a need for ranking functions that can, on one hand, attenuate the effects of link-spam without supervision and on the other hand, counter spam more aggressively when supervision is available. A family of non-linear, iteratively defined monotonic functions is proposed that propagates "rank" and "trust" scores through the webgraph. It relies on non-linearity, monotonicity and Schurconvexity to provide the resistance against spam. / text

Indiana University-Purdue University Indianapolis (IUPUI) / Web search has become a part of everyday life for hundreds of millions of users around the world. However, the effectiveness of a user's search depends vitally on the quality of search result ranking. Even though enormous efforts have been made to improve the ranking quality, there is still significant misalignment between search engine ranking and an end user's preference order. This is evident from the fact that, for many search results on major search and e-commerce platforms, many users ignore the top ranked results and click on the lower ranked results. Nevertheless, finding a ranking that suits all the users is a difficult problem to solve as every user's need is different. So, an ideal ranking is the one which is preferred by the majority of the users. This emphasizes the need for an automated approach which improves the search engine ranking dynamically by incorporating user clicks in the ranking algorithm. In existing search result ranking methodologies, this direction has not been explored profoundly. A key challenge in using user clicks in search result ranking is that the relevance feedback that is learnt from click data is imperfect. This is due to the fact that a user is more likely to click a top ranked result than a lower ranked result, irrespective of the actual relevance of those results. This phenomenon is known as position bias which poses a major difficulty in obtaining an automated method for dynamic update of search rank orders. In my thesis, I propose a set of methodologies which incorporate user clicks for dynamic update of search rank orders. The updates are based on adaptive randomization of results using reinforcement learning strategy by considering the user click activities as reinforcement signal. Beginning at any rank order of the search results, the proposed methodologies guaranty to converge to a ranking which is close to the ideal rank order. Besides, the usage of reinforcement learning strategy enables the proposed methods to overcome the position bias phenomenon. To measure the effectiveness of the proposed method, I perform experiments considering a simplified user behavior model which I call color ball abstraction model. I evaluate the quality of the proposed methodologies using standard information retrieval metrics like Precision at n (P@n), Kendall tau rank correlation, Discounted Cumulative Gain (DCG) and Normalized Discounted Cumulative Gain (NDCG). The experiment results clearly demonstrate the success of the proposed methodologies.

This doctoral thesis presents the findings of a mixed methods case study of Learning Skills, a new approach to Learning to Learn that was developed and implemented at a secondary school in the south of England between 2010 and 2014, and evaluated using data collected between 2009 and 2017. Learning to Learn is a field of educational theory and practice that aims to help young people get better at learning by focusing on the processes of learning (the how as well as the what), and by enabling them to take ownership over aspects of their own learning through activities such as goal setting, self-monitoring and structured reflection. The field has developed significantly throughout the last 40 years, with a number of approaches having been implemented on a large scale in the UK. Research into metacognition and self-regulation suggests that Learning to Learn programmes should help boost academic attainment. To date however, large-scale evaluations have found mixed results, with no clear impact on academic attainment. Using an intervention design used widely in medicine and other fields, Learning Skills reconceptualises Learning to Learn as a 'complex intervention' comprised of multiple areas of evidence-informed practice. The rationale for complex interventions is that the marginal gains emerging from any individual avenue of practice stack up and interact to yield a larger effect size overall. The Learning Skills programme, which started as a year seven taught course and developed into a whole-school approach to teaching and learning, focuses centrally on three key concepts: metacognition, self-regulation and oracy. This evaluation of Learning Skills incorporates eight strands of data collection and analysis over an eight-year period, using the previous year group at the same school as a control group. These include baseline measures; attitude to learning scores; psychometric questionnaires; a language of learning evaluation; reflective learning journals; student interviews; teacher interviews; and student attainment across all subjects in years nine and 11. The primary outcome analysis - student attainment across all subject areas at three and five years - found that Learning Skills cohort one achieved significantly higher grades than the control cohort, with accelerated gains among young people from economically disadvantaged backgrounds. Secondary data analysis incorporating a range of qualitative and quantitative methods indicates a causal relationship between Learning Skills and academic attainment. As well as evaluating the impact of a new and promising approach to Learning to Learn, this study generates new knowledge about the implementation and evaluation of complex interventions in education.

This research will examine the various techniques of writing stage directions for choreography or dance action within a feature film script. I will discuss and analyse two methodologies for annotating choreography, both developed by experts in dance notation. I will also examine and interpret the observations made by film director, dancer and choreographer Bob Fosse about the purpose and objectives of dance action in feature film scripts. I will examine two case studies of contemporary feature film scripts that contain dance action. The selected scripts are Strictly Ballroom (Australia, 1992) and Flashdance (USA, 1983). These scripts do not use a published system of dance notation to write dance action. I will analyse and investigate the stage directions for choreography and dance action used within both scripts. The exploration of these various approaches to film choreography may form the basis for writing stage directions for choreography or dance action in my own feature length screenplay titled Learning to Dance. As a screenwriter with particular interest in dance I intend to employ dance sequences at different stages throughout my script as a story telling mechanism. It is important to me to be able to clearly communicate and translate choreographic direction into my script in a manner that ensures its eventual interpretation fulfils its original purpose in the story. Therefore I am seeking a methodology for translating and expressing dance sequences in an accurate and concise written form. One key outcome of my research may be the development of a structural and technical framework for providing choreographic direction appropriate to the conventions of screenplay writing. I therefore intend to contribute to the screenwriting field by attempting to develop a framework for providing stage directions for choreography within a film script and then applying this framework within my own screenplay, Learning to Dance. In addition to the study of choreographic notation I will explore the observations made by film theorists such as Adrian Martin, Jerome Delamater, Rick Altman, J.P Telotte and Steve Neal about genres that contain dance action as a defining characteristic. It is my intention to write a screenplay that in part, borrows from the customs and codes of an established genre or subgenre. Therefore my objective is to understand, appreciate and reflect upon the genre the best fits my vision of Learning to Dance. Learning to Dance is the story of Giselle Williams (18) who aspires to be a professional dancer. When Giselle's father is arrested for fraud Giselle is forced to abandon her wealthy surrounds to live and work in one of Melbourne's tough, inner city, high-rise public housing estates. Here Giselle meets her key support roles, Muslim siblings Yasmina (21) a talented belly dancer and her handsome brother and Giselle's future love interest Ali (20) who welcome Giselle into their humble, tight knit and family oriented community.